Samsung's mobile and chip divisions are going in opposite directions, profit-wise.

AWS Lambda can now reference deployment packages directly in customer-owned S3 buckets, removing the per-Region code storage quota and raising the managed default from 75 GB to 300 GB. Per-function package limits are unchanged, and UpdateFunctionCode is still required after replacing an object. Terraform provider support remains an open enhancement request. By Steef-Jan Wiggers

Build a Profitable Amazon FBA Business - Abuv The Par Discussion | Link

Edit tracks & stems, get consistent music with new engine Discussion | Link

Duolingo for learning anything Discussion | Link

Gubernator Weekly Update: CoreDNS Aqueducts, SRE Stack, Network Topology & Cluster Auto-Updates! Gubernator Weekly Update Banner Review Gubernator Weekly Update Banner What an intense week for Gubernator (gbnt)! If you're new here, Gubernator is the "Goldilocks" container orchestrator that bridges the gap between Docker Swarm's simplicity (native Compose support, simple node joining) and Nomad's scheduling flexibility (hardware targeting, labels, task-based management). Over the past 7 days, Gubernator evolved from a single-node engine into a production-ready cluster ecosystem. Here is a breakdown of everything shipped this week! 1. Ingress & Service Discovery ("The Aqueducts") One of our biggest milestones this week was shipping automated internal DNS resolution and edge ingress routing: CoreDNS Integration: Every node running Gubernator can now deploy CoreDNS. Containers across all hosts can resolve internal service IPs using dynamic domain names (.gbnt.test). As containers spin up or die, Gubernator's manager updates CoreDNS records in real-time. Caddy Ingress: Exposing web services is now effortless. Services deployed with routing labels are automatically proxied by Caddy, managing SSL and HTTP/HTTPS ingress dynamically. 2. SRE Observability Suite (gbnt monitor init) Observability shouldn't require writing 500 lines of YAML. With a single command, gbnt monitor init, Gubernator deploys a complete, production-grade observability stack: Prometheus & cAdvisor: Detailed container and host-level metrics collection (CPU, RAM, Network I/O). Loki & Promtail: Centralized log aggregation across all containers. Grafana: Pre-configured dashboards for instant visualization out of the box. Jaeger Tracing: Full OpenTelemetry distributed tracing support (OTLP gRPC :4317 & HTTP :4318). Interactive Network Topology (Weave Scope Integration) Understanding how containers talk to each other across a distributed cluster can be tough. We integrated Weave Scope directly into the Flutter

Most VPN troubleshooting goes wrong in the same predictable way: three things get changed at once, and whatever happens next, nothing has been learned. The alternative is boring and effective — check one layer at a time, in an order that rules things out, and write down what each layer shows. One boundary before starting: troubleshooting means finding where a problem lives, not working against anyone's rules. On a network you don't control, or a device your organization manages, the policies in place stay in place. If a managed device is part of the picture, your organization's IT function is part of the troubleshooting — and switching off device security tooling is never a troubleshooting step. 1. Device basics first Start embarrassingly simple, because this layer resolves more than anyone likes to admit. Restart the VPN client. If that changes nothing, restart the device. Confirm that the operating system and the client are updated. An update that has been pending for weeks is a suspect, not background noise. Note whether anything changed around the time the problem started: an update, a new app, different settings, a different location. 2. Does the internet work without the VPN? Disconnect the VPN entirely and test ordinary browsing. If the connection is broken without the VPN, this isn't a VPN problem yet. Solve the underlying connection first, because nothing downstream is testable until this layer works. If the internet is fine without the VPN and wrong with it, you have genuinely narrowed something down. Write that down. 3. Client state: connected to what, exactly? Open the client and look, rather than assume. Is it actually connected, or still trying? Is the right profile selected — the current one, not an older entry left over from a previous setup? Disconnect and reconnect once, deliberately, and watch what the client reports. If multiple profiles have accumulated in the client, that is a finding in itself. Stale entries are a classic source of "it connect

Working with files in Linux isn't just about creating and editing them. Sometimes you need to: Archive multiple files into one Compress files to save disk space Share files with others Create backups Linux provides several tools for this, each with a different purpose. Let's simplify them. What is File Compression? File compression reduces the size of a file. Benefits: Saves disk space Faster file transfers Easier backups Reduces bandwidth usage Example: A 100 MB log file might become a much smaller compressed file, depending on its contents. Archive vs Compression Many beginners think they're the same. They are not. Archive Combines multiple files into a single file. Example: photos/ docs/ notes.txt ↓ backup.tar Compression Reduces the size of a file. Example: backup.tar ↓ backup.tar.gz 👉 tar archives files. gzip compresses them. 1. Create an Archive with tar tar -cvf backup.tar Documents/ #Create an archive tar -tvf backup.tar #View archive contents tar -xvf backup.tar #Extract an archive Options: c → Create v → Verbose (show progress) f → File name x → Extract Best for: Backups Bundling multiple files Moving folders 2. Compress with gzip Compress a file: gzip file.txt # Creates file.txt.gz # Result file.txt.gz gunzip file.txt.gz # decompress gzip -k file.txt # Keep original file Best for: Log files Large text files Saving disk space 3. Archive and Compress Together Most common command: # Create compressed archive tar -czvf backup.tar.gz Documents/ # Extract tar -xzvf backup.tar.gz Options: z → Use gzip compression 👉 This is one of the most common backup commands in Linux. 4. Working with ZIP Files # Create ZIP zip -r project.zip project/ # Extract unzip project.zip # List contents unzip -l project.zip Best for: Sharing files with Windows users Cross-platform compatibility 5. Compare the Tools Tool Purpose Best For tar Archive files Backups gzip Compress files Saving space tar + gzip Archive and compress Linux backups zip Archive and compress Sharing files across

Very few posts on this blog explain how to do something in software. On the blogs I ran before this one, and killed later, I wrote a lot more of that kind of content, because I needed it more back then. But I noticed that with AI, this need slowly went down for me too. At some point I asked myself why I don't go back to it, and how much sense that would even make. Just Ask Google I used to open Google, type my problem, and somehow find a solution close enough to what I needed. Now the numbers tell a different story. In 2026, less than a third of Google searches end with a click to any website. For the rest, AI Overviews already answer the question, so the user never leaves the search page. On queries where an AI Overview shows up, click rates sometimes drop as low as 17-20%. So which posts still get clicked? Not the "how do I do X" ones. Comparison posts and posts that share real experience get more clicks. The other type gets pulled out by AI and handed straight to the reader, without the blog in between. That's tiring, honestly. My writing gets picked up by AI before it even reaches my site, gets used, and my site gets no traffic from it. But does that actually matter? Is it worth quitting over? What Am I Even Writing? I sat down and went through my old posts, and a pattern showed up. I wasn't writing "how to use X." I was writing what happens when you actually use X in production, and what I learned from it. Turns out I was already doing the right thing for this era, without planning it. The first type is generic reference material. It's already in the docs, repeated in ten other blogs, and AI can summarize it faster than I ever could. The second type is something that happened to me. Which mistake I made, why I made it, how I noticed it. There's no documentation that can summarize that, because that experience only exists in me. Research backs this up too. AI isn't killing traffic, it's redistributing it. Clicks are dropping on generic, unbranded information que

I think many of us have already noticed that a growing number of open-source projects and libraries are moving towards commercial or dual-licensing models. In the .NET ecosystem, several widely used libraries have taken this path over the past year or so. AutoMapper and MediatR introduced commercial editions under a dual-licensing model, Fluent Assertions began requiring a paid licence for commercial use with version 8, and MassTransit 9 became a commercial product. These libraries were widely used in .NET applications and I mean widely used. Many projects treated them almost as a standard part of the ecosystem. Now, the same change is reaching the frontend world. PrimeTek recently announced that future major versions of PrimeNG, PrimeReact and PrimeVue will no longer be released as open source. All these projects were widely adopted, and many commercial applications depended heavily on them. Their licensing changes were primarily driven by the cost of long-term maintenance, but this raises a broader question: Is AI also changing the world of open source? You have probably already read many articles about code inflation. With AI, we can generate a huge amount of code in a very short time, even if the quality is sometimes questionable. The same thing is happening in open source. Maintainers can now receive more AI-generated issues, pull requests and feature requests than they can realistically review. Producing code has become cheaper, but understanding, testing and maintaining that code still requires significant human effort. Maintainers can become overwhelmed very quickly. AI may also discourage some developers from publishing their work publicly. Even small experiments, educational repositories and proof-of-concept projects can become training material for large language models. Some authors may therefore decide to keep their repositories private because they do not want AI companies learning from their work without permission, attribution or compensation. Licens

It's 3 AM. Your customer can't sleep. They open your SaaS product on their phone to check one number — whether their pipeline is healthy, whether something needs their attention before morning. What they see instead is a wall of 47 widgets, three unlabeled charts, and a date picker buried behind a gear icon. They close the tab. They don't come back. This isn't a hypothetical. In 2024, Userpilot benchmarked 62 B2B SaaS products and found that only 37.5% of new users ever reach activation — the point where they actually experience the value they signed up for ( Userpilot User Activation Benchmark Report, 2024 ). The rest poke around a dashboard, get overwhelmed, and leave. A Nielsen Norman Group study found that decision-makers spend roughly 2.3 seconds scanning a dashboard before deciding to engage or abandon it. Your analytics page is the screen where retention is won or lost. And most SaaS companies are losing. The Four Ways Dashboards Fail 1. The Data Dump The most common failure: treating a dashboard like a warehouse. Every stakeholder gets a tile. Three years in, the dashboard has 34 widgets and nobody can find anything. One UX audit of a banking analytics platform found that 11 of 23 displayed metrics were never clicked — four drove 80% of all sessions. After removing 17 widgets, adoption rose 41% in six weeks ( SaaS Dashboard Design: How to Build Dashboards Users Actually Love ). The team asked, "What data should we show?" The right question is: "What decision does this user need to make in the next 30 seconds?" 2. No Default Narrative A dashboard that shows "$42,000 MRR" with no trend arrow, no comparison, and no time period label forces the user to do mental math. A number without context is a snapshot; a number with a trend is a story. Research consistently suggests that 5–7 primary metrics is the maximum before cognitive load degrades comprehension — and for the headline view, 3–5 is ideal ( SaaS Dashboard Design Guidelines ). When different parts of a das